I fully understand the data partition in a nested k-fold CV. But reading this:
Within each outer fold, the best performing model was selected based on mean root mean squared error (RMSE) over the inner folds. The model was then retrained on all training and validation data from the inner folds and final generalization performance was evaluated on the held-out test data of the outer fold. Repeating this process for each outer fold yielded 3 best-performing models, and the mean test performance of these models is reported here...
I have a question: after the inner loop is complete, they've retrained the best model on the whole inner dataset (which makes sense, and is permittable since the tuning process hasn't "seen" the outer, test dataset) but in this retraining, which model over training epochs is selected now?
I believe there are two options: You'd treat the new test data as validation data and pick the best validation accuracy epoch during training,
or you can pick the best training accuracy epoch during training and test it independently on the test data.
Which one is it?